| 非基于测距的压缩感知多测量向量目标定位 |
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| 引用本文： | 郭艳,杨思星,李宁,孙保明,钱鹏.非基于测距的压缩感知多测量向量目标定位[J].计算机科学,2018,45(7):99-103. |
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| 作者姓名： | 郭艳 杨思星 李宁 孙保明 钱鹏 |
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| 作者单位： | 陆军工程大学通信工程学院 南京210007,陆军工程大学通信工程学院 南京210007,陆军工程大学通信工程学院 南京210007,陆军工程大学通信工程学院 南京210007,陆军工程大学通信工程学院 南京210007 |
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| 基金项目： | 本文受国家自然科学基金项目(61571463,61371124,61472445),江苏省自然科学基金(BK20171401)资助 |
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| 摘 要： | 传统的压缩感知定位方法大多是基于测距的,需要获得目标的精确定位信息,并不适用于资源受限的低损耗无线传感器网络。提出一种非基于测距的压缩感知多测量向量目标定位方法,能够大大降低对网络硬件的要求。该算法一方面根据传感器获得的目标连通性信息,设计了非基于测距的压缩感知定位模型；另一方面采用对定位区域进行动态感知的方法,解决了非基于测距的定位中定位精度不高的问题。该算法能够同时处理多组测量数据,且操作简单,适用性强。仿真证明,该算法具有较好的定位精确性和鲁棒性。
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| 关 键 词： | 压缩感知 多目标定位 多测量向量 非基于测距 |
| 收稿时间： | 2017/6/8 0:00:00 |
| 修稿时间： | 2017/9/6 0:00:00 |
Range-free Localization Based on Compressive Sensing Using Multiple Measurement Vectors |
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| GUO Yan,YANG Si-xing,LI Ning,SUN Bao-ming and QIAN Peng.Range-free Localization Based on Compressive Sensing Using Multiple Measurement Vectors[J].Computer Science,2018,45(7):99-103. |
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| Authors: | GUO Yan YANG Si-xing LI Ning SUN Bao-ming and QIAN Peng |
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| Affiliation: | Institute of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China,Institute of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China,Institute of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China,Institute of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China and Institute of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China |
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| Abstract: | Conventional compressive sensing based localization algorithms are always range-based,and need the accurate information of targets.As a result,they can not be applied in the low-lost system.This paper proposed a range-free localization algorithm beyond connectivity using multiple measurement vectors,which can largely reduce the localization errors.On one hand,this paper designed a new localization model for the range-free localization beyond wireless connectivity.On the other hand,it attempted to obtain multiple localization information in the area adaptively to improve the localization accuracy.The proposed algorithm is easy to operate and can be widely applied.Finally,it is proved that the new method has high accuracy and robustness. |
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| Keywords: | Compressive sensing Multiple target localization Multiple measurement vectors Range-free |
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